Overview

Dataset statistics

Number of variables22
Number of observations20115
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 MiB
Average record size in memory176.0 B

Variable types

Numeric10
Text5
DateTime2
Categorical5

Alerts

city has constant value ""Constant
state has constant value ""Constant
permit_number is highly overall correlated with expiration_dateHigh correlation
address_number_start is highly overall correlated with address_number and 3 other fieldsHigh correlation
address_number is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
ward is highly overall correlated with police_districtHigh correlation
police_district is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
latitude is highly overall correlated with address_number_start and 3 other fieldsHigh correlation
longitude is highly overall correlated with address_number_start and 2 other fieldsHigh correlation
expiration_date is highly overall correlated with permit_numberHigh correlation
street_type is highly imbalanced (52.2%)Imbalance
permit_number has unique valuesUnique
address_number_start has 1238 (6.2%) zerosZeros
address_number has 1238 (6.2%) zerosZeros

Reproduction

Analysis started2023-11-14 23:06:28.818126
Analysis finished2023-11-14 23:06:58.120908
Duration29.3 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

permit_number
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct20115
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1153906.5
Minimum1000571
Maximum1862206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:06:58.296542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1000571
5-th percentile1024747.2
Q11074657
median1106724
Q31131937.5
95-th percentile1647862.8
Maximum1862206
Range861635
Interquartile range (IQR)57280.5

Descriptive statistics

Standard deviation180107.75
Coefficient of variation (CV)0.15608523
Kurtosis5.2647718
Mean1153906.5
Median Absolute Deviation (MAD)25653
Skewness2.5385618
Sum2.3210829 × 1010
Variance3.2438803 × 1010
MonotonicityNot monotonic
2023-11-14T23:06:58.610538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1556602 1
 
< 0.1%
1086532 1
 
< 0.1%
1086551 1
 
< 0.1%
1086547 1
 
< 0.1%
1086546 1
 
< 0.1%
1086538 1
 
< 0.1%
1086537 1
 
< 0.1%
1086535 1
 
< 0.1%
1086534 1
 
< 0.1%
1086523 1
 
< 0.1%
Other values (20105) 20105
> 99.9%
ValueCountFrequency (%)
1000571 1
< 0.1%
1001307 1
< 0.1%
1002652 1
< 0.1%
1002993 1
< 0.1%
1003612 1
< 0.1%
1004393 1
< 0.1%
1007248 1
< 0.1%
1007265 1
< 0.1%
1007306 1
< 0.1%
1007406 1
< 0.1%
ValueCountFrequency (%)
1862206 1
< 0.1%
1860048 1
< 0.1%
1855208 1
< 0.1%
1854101 1
< 0.1%
1852797 1
< 0.1%
1848630 1
< 0.1%
1848204 1
< 0.1%
1846561 1
< 0.1%
1845400 1
< 0.1%
1844680 1
< 0.1%

account_number
Real number (ℝ)

Distinct3017
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205959.18
Minimum12
Maximum495456
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:06:59.026871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile5350
Q123077
median254059
Q3348409
95-th percentile414151
Maximum495456
Range495444
Interquartile range (IQR)325332

Descriptive statistics

Standard deviation156277.56
Coefficient of variation (CV)0.75877929
Kurtosis-1.6004864
Mean205959.18
Median Absolute Deviation (MAD)144576
Skewness-0.07715224
Sum4.1428689 × 109
Variance2.4422676 × 1010
MonotonicityNot monotonic
2023-11-14T23:06:59.272602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63414 886
 
4.4%
65004 312
 
1.6%
298727 114
 
0.6%
50161 85
 
0.4%
22633 66
 
0.3%
369504 64
 
0.3%
230211 61
 
0.3%
267891 48
 
0.2%
66022 45
 
0.2%
392906 44
 
0.2%
Other values (3007) 18390
91.4%
ValueCountFrequency (%)
12 6
 
< 0.1%
13 21
0.1%
16 4
 
< 0.1%
27 4
 
< 0.1%
28 11
0.1%
46 18
0.1%
51 6
 
< 0.1%
67 20
0.1%
73 17
0.1%
82 2
 
< 0.1%
ValueCountFrequency (%)
495456 1
< 0.1%
494737 1
< 0.1%
494267 1
< 0.1%
493688 1
< 0.1%
493621 1
< 0.1%
493348 1
< 0.1%
493334 1
< 0.1%
493192 1
< 0.1%
493107 1
< 0.1%
493069 1
< 0.1%

site_number
Real number (ℝ)

Distinct100
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3765846
Minimum1
Maximum230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:06:59.507958image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile23
Maximum230
Range229
Interquartile range (IQR)1

Descriptive statistics

Standard deviation18.125202
Coefficient of variation (CV)3.3711367
Kurtosis41.237255
Mean5.3765846
Median Absolute Deviation (MAD)0
Skewness5.9840194
Sum108150
Variance328.52294
MonotonicityNot monotonic
2023-11-14T23:06:59.762743image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 14712
73.1%
2 2297
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
7 121
 
0.6%
6 106
 
0.5%
11 98
 
0.5%
12 94
 
0.5%
19 91
 
0.5%
Other values (90) 1491
 
7.4%
ValueCountFrequency (%)
1 14712
73.1%
2 2297
 
11.4%
3 608
 
3.0%
4 287
 
1.4%
5 210
 
1.0%
6 106
 
0.5%
7 121
 
0.6%
8 65
 
0.3%
9 63
 
0.3%
10 67
 
0.3%
ValueCountFrequency (%)
230 1
< 0.1%
229 1
< 0.1%
228 1
< 0.1%
226 1
< 0.1%
225 1
< 0.1%
224 1
< 0.1%
221 1
< 0.1%
218 1
< 0.1%
217 1
< 0.1%
211 2
< 0.1%
Distinct3025
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:00.116547image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length67
Median length46
Mean length21.100224
Min length4

Characters and Unicode

Total characters424431
Distinct characters78
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique680 ?
Unique (%)3.4%

Sample

1st rowTHE LIFEWAY KEFIR SHOP LLC
2nd rowJERRY'S SANDWICHES LS, LLC
3rd rowGOMEZ RESTAURANT LLC
4th rowPLEASANT PIZZA, L.L.C.
5th rowMIR - MUR, INC.,
ValueCountFrequency (%)
inc 8856
 
12.9%
llc 6126
 
8.9%
corporation 1580
 
2.3%
restaurant 1296
 
1.9%
1263
 
1.8%
starbucks 886
 
1.3%
chicago 882
 
1.3%
the 862
 
1.3%
corp 859
 
1.2%
cafe 705
 
1.0%
Other values (3620) 45486
66.1%
2023-11-14T23:07:00.774175image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48842
 
11.5%
C 30244
 
7.1%
I 29080
 
6.9%
A 28334
 
6.7%
N 27601
 
6.5%
L 26453
 
6.2%
O 25667
 
6.0%
R 25230
 
5.9%
E 24953
 
5.9%
T 20568
 
4.8%
Other values (68) 137459
32.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 333457
78.6%
Space Separator 48842
 
11.5%
Other Punctuation 26490
 
6.2%
Lowercase Letter 7883
 
1.9%
Decimal Number 6929
 
1.6%
Dash Punctuation 645
 
0.2%
Close Punctuation 85
 
< 0.1%
Open Punctuation 85
 
< 0.1%
Math Symbol 15
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 30244
9.1%
I 29080
 
8.7%
A 28334
 
8.5%
N 27601
 
8.3%
L 26453
 
7.9%
O 25667
 
7.7%
R 25230
 
7.6%
E 24953
 
7.5%
T 20568
 
6.2%
S 20231
 
6.1%
Other values (16) 75096
22.5%
Lowercase Letter
ValueCountFrequency (%)
a 909
11.5%
e 905
11.5%
n 778
9.9%
o 653
8.3%
t 602
 
7.6%
r 591
 
7.5%
i 549
 
7.0%
s 513
 
6.5%
c 435
 
5.5%
l 353
 
4.5%
Other values (16) 1595
20.2%
Other Punctuation
ValueCountFrequency (%)
. 11714
44.2%
, 11204
42.3%
' 2276
 
8.6%
& 1097
 
4.1%
# 92
 
0.3%
/ 79
 
0.3%
" 18
 
0.1%
! 6
 
< 0.1%
@ 2
 
< 0.1%
: 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1325
19.1%
2 982
14.2%
0 893
12.9%
3 796
11.5%
5 795
11.5%
4 709
10.2%
8 465
 
6.7%
6 364
 
5.3%
7 356
 
5.1%
9 244
 
3.5%
Space Separator
ValueCountFrequency (%)
48842
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 645
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Math Symbol
ValueCountFrequency (%)
+ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 341340
80.4%
Common 83091
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 30244
 
8.9%
I 29080
 
8.5%
A 28334
 
8.3%
N 27601
 
8.1%
L 26453
 
7.7%
O 25667
 
7.5%
R 25230
 
7.4%
E 24953
 
7.3%
T 20568
 
6.0%
S 20231
 
5.9%
Other values (42) 82979
24.3%
Common
ValueCountFrequency (%)
48842
58.8%
. 11714
 
14.1%
, 11204
 
13.5%
' 2276
 
2.7%
1 1325
 
1.6%
& 1097
 
1.3%
2 982
 
1.2%
0 893
 
1.1%
3 796
 
1.0%
5 795
 
1.0%
Other values (16) 3167
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 424431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48842
 
11.5%
C 30244
 
7.1%
I 29080
 
6.9%
A 28334
 
6.7%
N 27601
 
6.5%
L 26453
 
6.2%
O 25667
 
6.0%
R 25230
 
5.9%
E 24953
 
5.9%
T 20568
 
4.8%
Other values (68) 137459
32.4%
Distinct3092
Distinct (%)15.4%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:01.137770image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length88
Median length45
Mean length16.847775
Min length1

Characters and Unicode

Total characters338893
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique669 ?
Unique (%)3.3%

Sample

1st rowLIFEWAY KEFIR SHOP
2nd rowJERRY'S SANDWICHES
3rd rowDON PEPE
4th rowBOB'S PIZZA
5th rowThe Great American Bagel
ValueCountFrequency (%)
2393
 
4.3%
cafe 1444
 
2.6%
the 1373
 
2.5%
coffee 1313
 
2.3%
restaurant 1269
 
2.3%
bar 1209
 
2.2%
grill 1046
 
1.9%
starbucks 904
 
1.6%
inc 729
 
1.3%
and 636
 
1.1%
Other values (3513) 43588
78.0%
2023-11-14T23:07:01.857244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35916
 
10.6%
A 27909
 
8.2%
E 25845
 
7.6%
R 20133
 
5.9%
O 19832
 
5.9%
S 19381
 
5.7%
I 17485
 
5.2%
T 17394
 
5.1%
N 16778
 
5.0%
C 14862
 
4.4%
Other values (67) 123358
36.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 268012
79.1%
Space Separator 35916
 
10.6%
Lowercase Letter 18888
 
5.6%
Other Punctuation 9371
 
2.8%
Decimal Number 6183
 
1.8%
Dash Punctuation 477
 
0.1%
Math Symbol 42
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 27909
 
10.4%
E 25845
 
9.6%
R 20133
 
7.5%
O 19832
 
7.4%
S 19381
 
7.2%
I 17485
 
6.5%
T 17394
 
6.5%
N 16778
 
6.3%
C 14862
 
5.5%
L 14448
 
5.4%
Other values (16) 73945
27.6%
Lowercase Letter
ValueCountFrequency (%)
e 2391
12.7%
a 2308
12.2%
o 1514
 
8.0%
n 1499
 
7.9%
r 1377
 
7.3%
i 1364
 
7.2%
t 1155
 
6.1%
l 1110
 
5.9%
s 1080
 
5.7%
u 717
 
3.8%
Other values (15) 4373
23.2%
Other Punctuation
ValueCountFrequency (%)
' 4092
43.7%
& 1842
19.7%
# 1296
 
13.8%
. 890
 
9.5%
, 579
 
6.2%
/ 572
 
6.1%
" 46
 
0.5%
! 43
 
0.5%
@ 8
 
0.1%
; 2
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1496
24.2%
1 936
15.1%
4 653
10.6%
3 600
9.7%
5 597
 
9.7%
0 569
 
9.2%
7 390
 
6.3%
6 361
 
5.8%
9 293
 
4.7%
8 288
 
4.7%
Space Separator
ValueCountFrequency (%)
35916
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 477
100.0%
Math Symbol
ValueCountFrequency (%)
+ 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 286900
84.7%
Common 51993
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 27909
 
9.7%
E 25845
 
9.0%
R 20133
 
7.0%
O 19832
 
6.9%
S 19381
 
6.8%
I 17485
 
6.1%
T 17394
 
6.1%
N 16778
 
5.8%
C 14862
 
5.2%
L 14448
 
5.0%
Other values (41) 92833
32.4%
Common
ValueCountFrequency (%)
35916
69.1%
' 4092
 
7.9%
& 1842
 
3.5%
2 1496
 
2.9%
# 1296
 
2.5%
1 936
 
1.8%
. 890
 
1.7%
4 653
 
1.3%
3 600
 
1.2%
5 597
 
1.1%
Other values (16) 3675
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
35916
 
10.6%
A 27909
 
8.2%
E 25845
 
7.6%
R 20133
 
5.9%
O 19832
 
5.9%
S 19381
 
5.7%
I 17485
 
5.2%
T 17394
 
5.1%
N 16778
 
5.0%
C 14862
 
4.4%
Other values (67) 123358
36.4%
Distinct2996
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-14T23:07:02.108379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:07:02.538556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

expiration_date
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
12/01/2017
 
1146
12/01/2016
 
1143
12/01/2015
 
1116
02/29/2020
 
1114
12/01/2014
 
1112
Other values (23)
14484 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters201150
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row02/28/2022
2nd row02/28/2022
3rd row02/28/2022
4th row02/28/2022
5th row02/28/2022

Common Values

ValueCountFrequency (%)
12/01/2017 1146
 
5.7%
12/01/2016 1143
 
5.7%
12/01/2015 1116
 
5.5%
02/29/2020 1114
 
5.5%
12/01/2014 1112
 
5.5%
12/01/2013 1083
 
5.4%
12/01/2012 1034
 
5.1%
12/01/2018 1022
 
5.1%
05/31/2021 1018
 
5.1%
12/01/2011 987
 
4.9%
Other values (18) 9340
46.4%

Length

2023-11-14T23:07:02.758559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12/01/2017 1146
 
5.7%
12/01/2016 1143
 
5.7%
12/01/2015 1116
 
5.5%
02/29/2020 1114
 
5.5%
12/01/2014 1112
 
5.5%
12/01/2013 1083
 
5.4%
12/01/2012 1034
 
5.1%
12/01/2018 1022
 
5.1%
05/31/2021 1018
 
5.1%
12/01/2011 987
 
4.9%
Other values (18) 9340
46.4%

Most occurring characters

ValueCountFrequency (%)
0 48382
24.1%
2 47084
23.4%
1 46786
23.3%
/ 40230
20.0%
8 3653
 
1.8%
3 3358
 
1.7%
5 2819
 
1.4%
9 2600
 
1.3%
4 2456
 
1.2%
7 1922
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160920
80.0%
Other Punctuation 40230
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48382
30.1%
2 47084
29.3%
1 46786
29.1%
8 3653
 
2.3%
3 3358
 
2.1%
5 2819
 
1.8%
9 2600
 
1.6%
4 2456
 
1.5%
7 1922
 
1.2%
6 1860
 
1.2%
Other Punctuation
ValueCountFrequency (%)
/ 40230
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 201150
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 48382
24.1%
2 47084
23.4%
1 46786
23.3%
/ 40230
20.0%
8 3653
 
1.8%
3 3358
 
1.7%
5 2819
 
1.4%
9 2600
 
1.3%
4 2456
 
1.2%
7 1922
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 201150
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 48382
24.1%
2 47084
23.4%
1 46786
23.3%
/ 40230
20.0%
8 3653
 
1.8%
3 3358
 
1.7%
5 2819
 
1.4%
9 2600
 
1.3%
4 2456
 
1.2%
7 1922
 
1.0%
Distinct2945
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
Minimum2001-03-14 00:00:00
Maximum2023-10-18 00:00:00
2023-11-14T23:07:02.958304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:07:03.216764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2575
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:03.584088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length34
Median length23
Mean length16.682675
Min length10

Characters and Unicode

Total characters335572
Distinct characters36
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique446 ?
Unique (%)2.2%

Sample

1st row0 W DIVISION ST
2nd row4739 N LINCOLN AVE
3rd row3616 W 26TH ST
4th row1659 W 21ST ST
5th row1154 W MADISON ST
ValueCountFrequency (%)
st 10458
 
12.9%
n 9114
 
11.2%
ave 8057
 
9.9%
w 7263
 
8.9%
e 1879
 
2.3%
s 1859
 
2.3%
0 1238
 
1.5%
clark 1189
 
1.5%
wells 1139
 
1.4%
lincoln 1043
 
1.3%
Other values (1881) 37931
46.7%
2023-11-14T23:07:04.220661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
61055
18.2%
E 21333
 
6.4%
S 21159
 
6.3%
A 20968
 
6.2%
N 20511
 
6.1%
T 16683
 
5.0%
1 12447
 
3.7%
L 12241
 
3.6%
I 11531
 
3.4%
W 11412
 
3.4%
Other values (26) 126232
37.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 206333
61.5%
Decimal Number 68184
 
20.3%
Space Separator 61055
 
18.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 21333
10.3%
S 21159
10.3%
A 20968
10.2%
N 20511
9.9%
T 16683
 
8.1%
L 12241
 
5.9%
I 11531
 
5.6%
W 11412
 
5.5%
R 10946
 
5.3%
O 10502
 
5.1%
Other values (15) 49047
23.8%
Decimal Number
ValueCountFrequency (%)
1 12447
18.3%
0 10234
15.0%
2 8735
12.8%
3 8200
12.0%
5 7626
11.2%
4 6592
9.7%
6 4474
 
6.6%
7 4027
 
5.9%
8 3100
 
4.5%
9 2749
 
4.0%
Space Separator
ValueCountFrequency (%)
61055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 206333
61.5%
Common 129239
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21333
10.3%
S 21159
10.3%
A 20968
10.2%
N 20511
9.9%
T 16683
 
8.1%
L 12241
 
5.9%
I 11531
 
5.6%
W 11412
 
5.5%
R 10946
 
5.3%
O 10502
 
5.1%
Other values (15) 49047
23.8%
Common
ValueCountFrequency (%)
61055
47.2%
1 12447
 
9.6%
0 10234
 
7.9%
2 8735
 
6.8%
3 8200
 
6.3%
5 7626
 
5.9%
4 6592
 
5.1%
6 4474
 
3.5%
7 4027
 
3.1%
8 3100
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 335572
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
61055
18.2%
E 21333
 
6.4%
S 21159
 
6.3%
A 20968
 
6.2%
N 20511
 
6.1%
T 16683
 
5.0%
1 12447
 
3.7%
L 12241
 
3.6%
I 11531
 
3.4%
W 11412
 
3.4%
Other values (26) 126232
37.6%

address_number_start
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1640
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.1723
Minimum0
Maximum11208
Zeros1238
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:04.537019image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1236
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.3622
Coefficient of variation (CV)0.97848173
Kurtosis1.3920877
Mean1664.1723
Median Absolute Deviation (MAD)1034
Skewness1.2201973
Sum33474826
Variance2651563.5
MonotonicityNot monotonic
2023-11-14T23:07:04.779354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1238
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1630) 17997
89.5%
ValueCountFrequency (%)
0 1238
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

address_number
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1640
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1664.1723
Minimum0
Maximum11208
Zeros1238
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:05.063045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300
median1236
Q32529
95-th percentile4857
Maximum11208
Range11208
Interquartile range (IQR)2229

Descriptive statistics

Standard deviation1628.3622
Coefficient of variation (CV)0.97848173
Kurtosis1.3920877
Mean1664.1723
Median Absolute Deviation (MAD)1034
Skewness1.2201973
Sum33474826
Variance2651563.5
MonotonicityNot monotonic
2023-11-14T23:07:05.675128image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1238
 
6.2%
200 152
 
0.8%
1 106
 
0.5%
111 104
 
0.5%
2100 95
 
0.5%
20 91
 
0.5%
400 90
 
0.4%
175 86
 
0.4%
731 81
 
0.4%
2200 75
 
0.4%
Other values (1630) 17997
89.5%
ValueCountFrequency (%)
0 1238
6.2%
1 106
 
0.5%
2 21
 
0.1%
5 17
 
0.1%
6 63
 
0.3%
7 7
 
< 0.1%
8 23
 
0.1%
9 17
 
0.1%
10 44
 
0.2%
11 9
 
< 0.1%
ValueCountFrequency (%)
11208 1
 
< 0.1%
11057 1
 
< 0.1%
10701 10
< 0.1%
10533 2
 
< 0.1%
10448 1
 
< 0.1%
9710 1
 
< 0.1%
8753 1
 
< 0.1%
8548 9
< 0.1%
8301 1
 
< 0.1%
8300 1
 
< 0.1%

street_direction
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
N
9114 
W
7263 
E
1879 
S
1859 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20115
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowW
2nd rowN
3rd rowW
4th rowW
5th rowW

Common Values

ValueCountFrequency (%)
N 9114
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Length

2023-11-14T23:07:05.911230image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:07:06.097337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
n 9114
45.3%
w 7263
36.1%
e 1879
 
9.3%
s 1859
 
9.2%

Most occurring characters

ValueCountFrequency (%)
N 9114
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 20115
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9114
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 20115
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9114
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20115
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9114
45.3%
W 7263
36.1%
E 1879
 
9.3%
S 1859
 
9.2%

street
Text

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:06.498017image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length24
Median length13
Mean length6.8899329
Min length3

Characters and Unicode

Total characters138591
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)0.1%

Sample

1st rowDIVISION
2nd rowLINCOLN
3rd row26TH
4th row21ST
5th rowMADISON
ValueCountFrequency (%)
clark 1189
 
5.7%
wells 1139
 
5.5%
lincoln 1043
 
5.0%
division 940
 
4.5%
michigan 729
 
3.5%
randolph 656
 
3.2%
southport 617
 
3.0%
milwaukee 599
 
2.9%
state 536
 
2.6%
halsted 500
 
2.4%
Other values (230) 12877
61.8%
2023-11-14T23:07:07.120421image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 12911
 
9.3%
L 11717
 
8.5%
I 11531
 
8.3%
E 11397
 
8.2%
N 11397
 
8.2%
O 10502
 
7.6%
R 10142
 
7.3%
S 8882
 
6.4%
T 6193
 
4.5%
D 6011
 
4.3%
Other values (25) 37908
27.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 136954
98.8%
Decimal Number 927
 
0.7%
Space Separator 710
 
0.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 12911
 
9.4%
L 11717
 
8.6%
I 11531
 
8.4%
E 11397
 
8.3%
N 11397
 
8.3%
O 10502
 
7.7%
R 10142
 
7.4%
S 8882
 
6.5%
T 6193
 
4.5%
D 6011
 
4.4%
Other values (15) 36271
26.5%
Decimal Number
ValueCountFrequency (%)
3 310
33.4%
5 226
24.4%
1 143
15.4%
8 60
 
6.5%
6 56
 
6.0%
7 49
 
5.3%
2 44
 
4.7%
9 26
 
2.8%
4 13
 
1.4%
Space Separator
ValueCountFrequency (%)
710
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136954
98.8%
Common 1637
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 12911
 
9.4%
L 11717
 
8.6%
I 11531
 
8.4%
E 11397
 
8.3%
N 11397
 
8.3%
O 10502
 
7.7%
R 10142
 
7.4%
S 8882
 
6.5%
T 6193
 
4.5%
D 6011
 
4.4%
Other values (15) 36271
26.5%
Common
ValueCountFrequency (%)
710
43.4%
3 310
18.9%
5 226
 
13.8%
1 143
 
8.7%
8 60
 
3.7%
6 56
 
3.4%
7 49
 
3.0%
2 44
 
2.7%
9 26
 
1.6%
4 13
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138591
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 12911
 
9.3%
L 11717
 
8.5%
I 11531
 
8.3%
E 11397
 
8.2%
N 11397
 
8.2%
O 10502
 
7.6%
R 10142
 
7.3%
S 8882
 
6.4%
T 6193
 
4.5%
D 6011
 
4.3%
Other values (25) 37908
27.4%

street_type
Categorical

IMBALANCE 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
ST
10418 
AVE
8057 
RD
 
608
BLVD
 
269
PL
 
255
Other values (4)
 
508

Length

Max length4
Median length2
Mean length2.4491176
Min length2

Characters and Unicode

Total characters49264
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST
2nd rowAVE
3rd rowST
4th rowST
5th rowST

Common Values

ValueCountFrequency (%)
ST 10418
51.8%
AVE 8057
40.1%
RD 608
 
3.0%
BLVD 269
 
1.3%
PL 255
 
1.3%
PKWY 199
 
1.0%
DR 196
 
1.0%
CT 72
 
0.4%
HWY 41
 
0.2%

Length

2023-11-14T23:07:07.377405image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:07:07.622649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
st 10418
51.8%
ave 8057
40.1%
rd 608
 
3.0%
blvd 269
 
1.3%
pl 255
 
1.3%
pkwy 199
 
1.0%
dr 196
 
1.0%
ct 72
 
0.4%
hwy 41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
T 10490
21.3%
S 10418
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 49264
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 10490
21.3%
S 10418
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 49264
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 10490
21.3%
S 10418
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 10490
21.3%
S 10418
21.1%
V 8326
16.9%
A 8057
16.4%
E 8057
16.4%
D 1073
 
2.2%
R 804
 
1.6%
L 524
 
1.1%
P 454
 
0.9%
B 269
 
0.5%
Other values (5) 792
 
1.6%

city
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
CHICAGO
20115 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters140805
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHICAGO
2nd rowCHICAGO
3rd rowCHICAGO
4th rowCHICAGO
5th rowCHICAGO

Common Values

ValueCountFrequency (%)
CHICAGO 20115
100.0%

Length

2023-11-14T23:07:07.880420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:07:08.033131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
chicago 20115
100.0%

Most occurring characters

ValueCountFrequency (%)
C 40230
28.6%
H 20115
14.3%
I 20115
14.3%
A 20115
14.3%
G 20115
14.3%
O 20115
14.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 140805
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 40230
28.6%
H 20115
14.3%
I 20115
14.3%
A 20115
14.3%
G 20115
14.3%
O 20115
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 140805
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 40230
28.6%
H 20115
14.3%
I 20115
14.3%
A 20115
14.3%
G 20115
14.3%
O 20115
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 40230
28.6%
H 20115
14.3%
I 20115
14.3%
A 20115
14.3%
G 20115
14.3%
O 20115
14.3%

state
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
IL
20115 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters40230
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIL
2nd rowIL
3rd rowIL
4th rowIL
5th rowIL

Common Values

ValueCountFrequency (%)
IL 20115
100.0%

Length

2023-11-14T23:07:08.192494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-14T23:07:08.358464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
il 20115
100.0%

Most occurring characters

ValueCountFrequency (%)
I 20115
50.0%
L 20115
50.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 40230
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 20115
50.0%
L 20115
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 40230
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 20115
50.0%
L 20115
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40230
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 20115
50.0%
L 20115
50.0%

zip_code
Real number (ℝ)

Distinct53
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60625.512
Minimum60601
Maximum60707
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:08.566467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum60601
5-th percentile60602
Q160610
median60618
Q360647
95-th percentile60657
Maximum60707
Range106
Interquartile range (IQR)37

Descriptive statistics

Standard deviation19.889225
Coefficient of variation (CV)0.00032806692
Kurtosis-0.68247276
Mean60625.512
Median Absolute Deviation (MAD)11
Skewness0.70024692
Sum1.2194822 × 109
Variance395.58127
MonotonicityNot monotonic
2023-11-14T23:07:08.997187image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60611 1840
 
9.1%
60654 1768
 
8.8%
60622 1723
 
8.6%
60614 1550
 
7.7%
60657 1420
 
7.1%
60607 1156
 
5.7%
60613 981
 
4.9%
60610 948
 
4.7%
60647 883
 
4.4%
60618 835
 
4.2%
Other values (43) 7011
34.9%
ValueCountFrequency (%)
60601 652
3.2%
60602 410
 
2.0%
60603 330
 
1.6%
60604 234
 
1.2%
60605 729
3.6%
60606 385
 
1.9%
60607 1156
5.7%
60608 207
 
1.0%
60609 31
 
0.2%
60610 948
4.7%
ValueCountFrequency (%)
60707 47
 
0.2%
60661 504
 
2.5%
60660 199
 
1.0%
60659 189
 
0.9%
60657 1420
7.1%
60656 15
 
0.1%
60655 3
 
< 0.1%
60654 1768
8.8%
60653 20
 
0.1%
60651 3
 
< 0.1%

ward
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.483619
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:09.242811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q126
median42
Q343
95-th percentile47
Maximum50
Range49
Interquartile range (IQR)17

Descriptive statistics

Standard deviation16.40505
Coefficient of variation (CV)0.52106619
Kurtosis-0.6761582
Mean31.483619
Median Absolute Deviation (MAD)5
Skewness-0.95202913
Sum633293
Variance269.12565
MonotonicityNot monotonic
2023-11-14T23:07:09.465937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
42 5554
27.6%
1 1579
 
7.8%
27 1537
 
7.6%
47 1476
 
7.3%
44 1427
 
7.1%
2 1374
 
6.8%
43 1186
 
5.9%
32 1114
 
5.5%
4 666
 
3.3%
25 471
 
2.3%
Other values (34) 3731
18.5%
ValueCountFrequency (%)
1 1579
7.8%
2 1374
6.8%
3 289
 
1.4%
4 666
3.3%
5 103
 
0.5%
6 1
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
10 13
 
0.1%
11 331
 
1.6%
ValueCountFrequency (%)
50 104
 
0.5%
49 148
 
0.7%
48 361
 
1.8%
47 1476
 
7.3%
46 335
 
1.7%
45 238
 
1.2%
44 1427
 
7.1%
43 1186
 
5.9%
42 5554
27.6%
41 81
 
0.4%

police_district
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.841163
Minimum0
Maximum25
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:09.706473image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q112
median18
Q319
95-th percentile20
Maximum25
Range25
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.8663266
Coefficient of variation (CV)0.49608017
Kurtosis-0.40064214
Mean13.841163
Median Absolute Deviation (MAD)2
Skewness-1.0016315
Sum278415
Variance47.146441
MonotonicityNot monotonic
2023-11-14T23:07:09.983481image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 5337
26.5%
19 4255
21.2%
1 3656
18.2%
12 2387
11.9%
14 1788
 
8.9%
20 760
 
3.8%
16 418
 
2.1%
17 373
 
1.9%
24 309
 
1.5%
2 237
 
1.2%
Other values (13) 595
 
3.0%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 3656
18.2%
2 237
 
1.2%
3 10
 
< 0.1%
4 15
 
0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
8 44
 
0.2%
9 223
 
1.1%
ValueCountFrequency (%)
25 149
 
0.7%
24 309
 
1.5%
22 27
 
0.1%
20 760
 
3.8%
19 4255
21.2%
18 5337
26.5%
17 373
 
1.9%
16 418
 
2.1%
15 7
 
< 0.1%
14 1788
 
8.9%

latitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.910375
Minimum41.69067
Maximum42.019421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:10.233412image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum41.69067
5-th percentile41.865345
Q141.885257
median41.902442
Q341.939274
95-th percentile41.978062
Maximum42.019421
Range0.32875146
Interquartile range (IQR)0.05401635

Descriptive statistics

Standard deviation0.037933015
Coefficient of variation (CV)0.00090509843
Kurtosis1.7970542
Mean41.910375
Median Absolute Deviation (MAD)0.020466559
Skewness-0.10661154
Sum843027.2
Variance0.0014389136
MonotonicityNot monotonic
2023-11-14T23:07:10.474478image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.88200199 98
 
0.5%
41.88197573 96
 
0.5%
41.90405052 85
 
0.4%
41.87801449 81
 
0.4%
41.88460018 69
 
0.3%
41.8825402 53
 
0.3%
41.90186736 40
 
0.2%
41.89678605 40
 
0.2%
41.88216417 37
 
0.2%
41.87949547 37
 
0.2%
Other values (2557) 19479
96.8%
ValueCountFrequency (%)
41.69066951 1
 
< 0.1%
41.69139989 2
 
< 0.1%
41.69245222 1
 
< 0.1%
41.69920305 10
< 0.1%
41.70289718 1
 
< 0.1%
41.70356373 2
 
< 0.1%
41.71874411 1
 
< 0.1%
41.72107515 10
< 0.1%
41.72112515 1
 
< 0.1%
41.72177014 1
 
< 0.1%
ValueCountFrequency (%)
42.01942097 12
0.1%
42.0193885 5
 
< 0.1%
42.01934594 4
 
< 0.1%
42.01933013 3
 
< 0.1%
42.01932963 2
 
< 0.1%
42.0193098 4
 
< 0.1%
42.01927235 1
 
< 0.1%
42.0174068 8
< 0.1%
42.01615704 15
0.1%
42.01615267 15
0.1%

longitude
Real number (ℝ)

HIGH CORRELATION 

Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.6553
Minimum-87.834308
Maximum-87.535139
Zeros0
Zeros (%)0.0%
Negative20115
Negative (%)100.0%
Memory size157.3 KiB
2023-11-14T23:07:10.782407image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-87.834308
5-th percentile-87.709805
Q1-87.672586
median-87.648962
Q3-87.629547
95-th percentile-87.624223
Maximum-87.535139
Range0.29916895
Interquartile range (IQR)0.043038819

Descriptive statistics

Standard deviation0.033389325
Coefficient of variation (CV)-0.00038091621
Kurtosis5.1242084
Mean-87.6553
Median Absolute Deviation (MAD)0.020372619
Skewness-1.7591271
Sum-1763186.4
Variance0.001114847
MonotonicityNot monotonic
2023-11-14T23:07:11.093087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.63103164 98
 
0.5%
-87.63397185 96
 
0.5%
-87.62874675 85
 
0.4%
-87.63318903 81
 
0.4%
-87.62798897 69
 
0.3%
-87.62453095 53
 
0.3%
-87.62849214 40
 
0.2%
-87.62828088 40
 
0.2%
-87.62451427 37
 
0.2%
-87.63382966 37
 
0.2%
Other values (2557) 19479
96.8%
ValueCountFrequency (%)
-87.8343079 1
 
< 0.1%
-87.82625503 9
< 0.1%
-87.82618448 1
 
< 0.1%
-87.82167425 5
 
< 0.1%
-87.82042719 4
 
< 0.1%
-87.81865423 16
0.1%
-87.81795264 4
 
< 0.1%
-87.81783297 3
 
< 0.1%
-87.81729036 11
0.1%
-87.8172596 1
 
< 0.1%
ValueCountFrequency (%)
-87.53513895 2
 
< 0.1%
-87.55117213 1
 
< 0.1%
-87.55124869 1
 
< 0.1%
-87.55161886 9
< 0.1%
-87.56729719 2
 
< 0.1%
-87.58184369 4
< 0.1%
-87.58390766 1
 
< 0.1%
-87.58502961 2
 
< 0.1%
-87.58781452 8
< 0.1%
-87.58797399 7
< 0.1%
Distinct2567
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size157.3 KiB
2023-11-14T23:07:11.431025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length40
Median length39
Mean length39.105543
Min length35

Characters and Unicode

Total characters786608
Distinct characters16
Distinct categories6 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique445 ?
Unique (%)2.2%

Sample

1st row(41.90405051948726, -87.62874675447662)
2nd row(41.96769881732379, -87.68780818484225)
3rd row(41.844483527070835, -87.71558453559561)
4th row(41.853999857174315, -87.66845450091006)
5th row(41.88173703022226, -87.65654694665614)
ValueCountFrequency (%)
41.88200198545344 98
 
0.2%
87.6310316367502 98
 
0.2%
41.881975727713886 96
 
0.2%
87.63397184627037 96
 
0.2%
41.90405051948726 85
 
0.2%
87.62874675447662 85
 
0.2%
41.878014487249544 81
 
0.2%
87.63318903001444 81
 
0.2%
87.62798896732363 69
 
0.2%
41.884600177780484 69
 
0.2%
Other values (5124) 39372
97.9%
2023-11-14T23:07:12.035125image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 84455
10.7%
4 75167
9.6%
7 74159
9.4%
6 72012
9.2%
1 70014
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53203
 
6.8%
5 52643
 
6.7%
0 47735
 
6.1%
Other values (6) 140805
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 645803
82.1%
Other Punctuation 60345
 
7.7%
Open Punctuation 20115
 
2.6%
Space Separator 20115
 
2.6%
Dash Punctuation 20115
 
2.6%
Close Punctuation 20115
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 84455
13.1%
4 75167
11.6%
7 74159
11.5%
6 72012
11.2%
1 70014
10.8%
9 62712
9.7%
2 53703
8.3%
3 53203
8.2%
5 52643
8.2%
0 47735
7.4%
Other Punctuation
ValueCountFrequency (%)
. 40230
66.7%
, 20115
33.3%
Open Punctuation
ValueCountFrequency (%)
( 20115
100.0%
Space Separator
ValueCountFrequency (%)
20115
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20115
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20115
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 786608
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
8 84455
10.7%
4 75167
9.6%
7 74159
9.4%
6 72012
9.2%
1 70014
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53203
 
6.8%
5 52643
 
6.7%
0 47735
 
6.1%
Other values (6) 140805
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 786608
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 84455
10.7%
4 75167
9.6%
7 74159
9.4%
6 72012
9.2%
1 70014
8.9%
9 62712
8.0%
2 53703
 
6.8%
3 53203
 
6.8%
5 52643
 
6.7%
0 47735
 
6.1%
Other values (6) 140805
17.9%

Interactions

2023-11-14T23:06:55.188559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:32.571633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:34.780475image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:37.144990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:39.630698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:43.184041image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:46.675370image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.765508image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:51.103592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:53.141722image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:55.367695image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:32.865784image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:34.944012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:37.329585image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:39.848384image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:43.669304image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:46.885647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.938103image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:51.314028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:53.364269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:55.523226image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.067729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:35.177643image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:37.534942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:40.104358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:44.173589image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:47.100005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:49.100678image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:51.570529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:53.608551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:55.769298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.203908image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:35.517826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:37.758345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:40.304965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:44.703796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:47.280897image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:49.370247image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:51.725918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:53.779117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:55.965050image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.402981image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:35.778533image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:37.937394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:40.564567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:45.152810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:47.441998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:49.555567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:51.869178image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.046196image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:56.110730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.581059image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:35.974437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:38.101270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:40.826993image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:45.409877image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:47.621788image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:49.974052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:52.040544image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.266868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:56.283690image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.762903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:36.253865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:38.435486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:41.164969image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:45.740983image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.047106image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:50.373944image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:52.223979image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.430494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:56.475340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:33.971467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:36.479464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:38.861567image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:41.539997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:45.967934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.233629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:50.547124image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:52.461854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.627575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:56.651372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:34.212934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:36.692098image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:39.116805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:42.001848image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:46.197192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.395858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:50.697940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:52.650586image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.809391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:56.806309image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:34.498857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:36.913749image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:39.405674image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:42.593624image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:46.420912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:48.575625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:50.872985image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:52.887761image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-14T23:06:54.979702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-14T23:07:12.280968image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
permit_numberaccount_numbersite_numberaddress_number_startaddress_numberzip_codewardpolice_districtlatitudelongitudeexpiration_datestreet_directionstreet_type
permit_number1.0000.4730.018-0.174-0.174-0.018-0.082-0.156-0.1430.0390.8720.0370.041
account_number0.4731.000-0.174-0.123-0.123-0.046-0.108-0.158-0.1360.0210.2700.0750.067
site_number0.018-0.1741.000-0.122-0.122-0.0970.075-0.051-0.0740.1440.0410.0690.054
address_number_start-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7450.0750.2940.210
address_number-0.174-0.123-0.1221.0001.0000.3780.1760.5130.707-0.7450.0750.2940.210
zip_code-0.018-0.046-0.0970.3780.3781.0000.1770.4520.450-0.4760.0250.3070.222
ward-0.082-0.1080.0750.1760.1760.1771.0000.5100.453-0.0490.0310.2860.167
police_district-0.156-0.158-0.0510.5130.5130.4520.5101.0000.813-0.3390.0790.3830.186
latitude-0.143-0.136-0.0740.7070.7070.4500.4530.8131.000-0.6330.0880.3850.212
longitude0.0390.0210.144-0.745-0.745-0.476-0.049-0.339-0.6331.0000.0450.3210.268
expiration_date0.8720.2700.0410.0750.0750.0250.0310.0790.0880.0451.0000.0260.017
street_direction0.0370.0750.0690.2940.2940.3070.2860.3830.3850.3210.0261.0000.241
street_type0.0410.0670.0540.2100.2100.2220.1670.1860.2120.2680.0170.2411.000

Missing values

2023-11-14T23:06:57.128851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-14T23:06:57.725339image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
015566023289921THE LIFEWAY KEFIR SHOP LLCLIFEWAY KEFIR SHOP07/16/202102/28/202207/16/20210 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.0118.041.904051-87.628747(41.90405051948726, -87.62874675447662)
115313033994981JERRY'S SANDWICHES LS, LLCJERRY'S SANDWICHES07/16/202102/28/202207/16/20214739 N LINCOLN AVE47394739NLINCOLNAVECHICAGOIL60625.04719.041.967699-87.687808(41.96769881732379, -87.68780818484225)
215482403696671GOMEZ RESTAURANT LLCDON PEPE07/20/202102/28/202207/20/20213616 W 26TH ST36163616W26THSTCHICAGOIL60623.02210.041.844484-87.715585(41.844483527070835, -87.71558453559561)
315313184314221PLEASANT PIZZA, L.L.C.BOB'S PIZZA07/23/202102/28/202207/23/20211659 W 21ST ST16591659W21STSTCHICAGOIL60608.02512.041.854000-87.668455(41.853999857174315, -87.66845450091006)
415338424579921MIR - MUR, INC.,The Great American Bagel07/23/202102/28/202207/23/20211154 W MADISON ST11541154WMADISONSTCHICAGOIL60607.02512.041.881737-87.656547(41.88173703022226, -87.65654694665614)
515485244575931ODA RESTAURANT LLCODA MEDITERRANEAN CUISINE07/23/202102/28/202207/23/20215657 N CLARK ST56575657NCLARKSTCHICAGOIL60660.04820.041.984972-87.668929(41.98497216927604, -87.66892851302207)
615488492113241ITALIAN RISTORANTE-HUBBARD, LLCVERMILION12/18/202102/28/202212/18/202110 W HUBBARD ST1010WHUBBARDSTCHICAGOIL60654.04218.041.890169-87.628394(41.89016858549094, -87.62839433601951)
716859953609521AMY'S CANDY BAR INC.Amy's Candy Bar06/14/202202/28/202306/14/20220 N DAMEN AVE00NDAMENAVECHICAGOIL60625.04712.041.881359-87.676789(41.881358617173056, -87.67678939121616)
816439694631881ETTA RIVER NORTH, LLCETTA03/09/202202/28/202302/24/20220 N CLARK ST00NCLARKSTCHICAGOIL60654.021.041.882002-87.631032(41.88200198545344, -87.6310316367502)
916409923704674GEB, LLCTHE BANDIT03/09/202202/28/202303/09/20220 W RANDOLPH ST00WRANDOLPHSTCHICAGOIL60607.0271.041.884600-87.627989(41.884600177780484, -87.62798896732363)
permit_numberaccount_numbersite_numberlegal_namedoing_business_as_nameissued_dateexpiration_datepayment_dateaddressaddress_number_startaddress_numberstreet_directionstreetstreet_typecitystatezip_codewardpolice_districtlatitudelongitudelocation
2010518067943082711KATHY A DECARLOKATHY DE'S06/20/202302/29/202406/20/20233642 S PARNELL AVE36423642SPARNELLAVECHICAGOIL60609.0119.041.827725-87.640085(41.82772496307889, -87.64008519409887)
2010617821914217672DOLLOP WELLS LLCDollop Coffee bar06/20/202302/29/202406/20/20230 N WELLS ST00NWELLSSTCHICAGOIL60610.0271.041.881976-87.633972(41.881975727713886, -87.63397184627037)
2010718236174685601BUREAU BAR AND RESTAURANT LLCBUREAU BAR AND RESTAURANT06/21/202302/29/202406/21/20230 S STATE ST00SSTATESTCHICAGOIL60616.031.041.881960-87.627937(41.881960316026536, -87.6279372789252)
2010818090454869151River North Egg Harbor, LLCEgg Harbor Cafe06/21/202302/29/202406/21/2023800 N WELLS ST800800NWELLSSTCHICAGOIL60610.02718.041.896634-87.634351(41.8966337490024, -87.63435133722824)
2010918137423845621TACOS TEQUILAS ON THE LAKE, INC.TACOS TEQUILAS06/21/202302/29/202406/21/20232919 N MILWAUKEE AVE29192919NMILWAUKEEAVECHICAGOIL60618.03514.041.934393-87.715900(41.934392894503155, -87.71589988857131)
2011018160174902291LA ESQUINA DEL TACO INC.,LA ESQUINA DEL TACO06/21/202302/29/202406/21/20233259 W 63RD ST32593259W63RDSTCHICAGOIL60629.0148.041.778828-87.705434(41.77882757627881, -87.70543426650146)
2011118272024826042PWU DUMMY ACCOUNTPWU DUMMY ACCOUNT06/22/202302/29/202406/22/20233328 N LINCOLN AVE33283328NLINCOLNAVECHICAGOIL60657.03219.041.942385-87.670714(41.94238547241118, -87.67071383788313)
20112182862075331LA BRUQUENA RESTAURANT & LOUNGE, INC.LA BRUQUENA RESTAURANT & LOUNGE06/23/202302/29/202406/23/20230 W DIVISION ST00WDIVISIONSTCHICAGOIL60622.02618.041.904051-87.628747(41.90405051948726, -87.62874675447662)
2011318156702943291RICHMOND TAVERN, INC.RICHMOND TAVERN06/27/202302/29/202406/27/20232944 W GRAND AVE29442944WGRANDAVECHICAGOIL60622.03612.041.896089-87.700748(41.89608917059594, -87.70074823336402)
2011418045364745392PEDESTRIAN COFFEE LLCTHE COFFEE STUDIO06/27/202302/29/202406/27/20235628 N CLARK ST56285628NCLARKSTCHICAGOIL60660.04020.041.984385-87.669099(41.984384824892075, -87.66909931387195)